Amdahl’s Law
Amdahl’s Law describes how the speedup from optimizing or parallelizing a computation is limited by the fraction that remains sequential or unoptimized.
Grokking Concurrency
Amdahl's law is a formula for estimating the maximum speedup of a computation from its sequential fraction and available processing resources.
- How it is used
- Estimates whether parallelizing a program is worthwhile by comparing sequential work with independently executable parallel work.
- How deep it goes
- Explains the speedup limit through a timed card-search example, stopping short of detailed formula derivation or implementation.
- Examples
- Uses a mother-and-baby analogy and a timed index-card search divided among friends.
- What you can do after
- Evaluate expected parallelization benefits before redesigning an algorithm for multiple processing resources.
Learn Concurrent Programming with Go
A performance model stating that the overall speedup from optimizing or parallelizing a system is limited by the fraction of execution that remains sequential.
- How it is used
- Guides decisions about whether optimizing or parallelizing a workload can deliver meaningful performance gains.
- How deep it goes
- Defines sequential bottlenecks and illustrates speedup limits with construction scenarios and a processor-scaling figure, without deriving the formula.
- Examples
- Uses house construction, a narrow transport route, processor-count charts, and a 95%-parallel workload reaching roughly 19-fold speedup.
- What you can do after
- Evaluate scalability limits and choose whether additional parallel resources are likely to improve a fixed-size workload.
Read how Learn Concurrent Programming with Go explains Amdahl’s Law →
Latency
Amdahl's law is a performance model that estimates the maximum speedup from parallelizing a fixed-size computation according to its parallelizable fraction and number of execution units.
- How it is used
- Guides latency design by estimating how much parallel processing can accelerate a fixed-size computation.
- How deep it goes
- Defines theoretical speedup, applies it to an endpoint, and stops short of replacing empirical latency measurement.
- Examples
- Uses an order-processing endpoint, a 5/15/30 ms timing breakdown, and speedup curves across parallelizable fractions.
- What you can do after
- Evaluate parallelization limits and validate latency assumptions through controlled measurement.
Related concepts
- speedupmeasures · Speedup is the ratio of a baseline execution time to an alternative execution time for the same computation.
- parallelismuses · Parallelism is the concurrent execution of multiple computational operations, often across multiple processing resources.
- meanuses · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
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